Determinants of AI Adoption Intention among Open and Distance Learning Practitioners in Malaysian Public Education: A Preliminary Study

The rapid expansion of open and distance learning across Malaysian public higher education institutions, accelerated by the COVID-19 pandemic and mandated through the national Digital Education Policy, has created an urgent need to understand what motivates ODL practitioners to adopt artificial intelligence tools. The study investigates the determinants of artificial intelligence adoption intention among Open and Distance Learning practitioners in Malaysian public education institutions by extending the Technology Acceptance Model with institutional expectation, proactive AI behaviour, and prior AI experience. A quantitative cross-sectional survey was conducted involving 60 AI users employed across the Ministry of Education, Ministry of Higher Education, public universities, and affiliated educational agencies. Data were analysed using descriptive statistics, reliability analysis, Pearson correlation, and multiple regression analysis. Findings reveal that perceived usefulness and proactive AI behaviour are the two strongest significant predictors of AI adoption intention, collectively supporting an extended TAM framework that explains 77.4% of the variance in behavioural intention. In contrast, perceived ease of use, institutional expectation, and prior AI experience were not statistically significant in the controlled regression model, although institutional expectation demonstrated a practically meaningful descriptive trend. The study further identifies a substantial AI adoption depth gap whereby chatbot and conversational AI usage is nearly universal, while adoption of pedagogically transformative applications such as predictive analytics, recommendation systems, and adaptive learning tools remains minimal. These findings suggest that Malaysian ODL practitioners are digitally engaged with AI but have not fully transitioned toward applications capable of improving learner retention, personalised learning, and scalable learner support. The study contributes theoretically by positioning proactive AI behaviour as an independent dispositional pathway influencing AI adoption intention beyond traditional TAM constructs. Practical implications are provided for policymakers, higher education institutions, and quality assurance agencies, particularly regarding the strategic enhancement of AI-driven ODL quality initiatives in Malaysia.